GraphRAG-R1

This is the official repository for the paper “GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learning” (Accepted to WWW '26).

Item	Details
📄 Paper	GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learning
🏆 Conference	The ACM Web Conference 2026 (WWW '26) April 13–17, 2026, Dubai, United Arab Emirates
💻 Full Code	GitHub: (https://github.com/ycygit/GraphRAG-R1)
🤗 HF Models	This repository provides the LoRA adapters. See Model Weights & Usage.

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🔍 Quick Links

• Abstract
• Model Weights & Usage
• Citation
• Contact

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📝 Abstract

GraphRAG-R1 is a Graph Retrieval-Augmented Generation framework enhanced with Process-Constrained Reinforcement Learning. It is designed to significantly improve the reasoning capabilities of large language models (LLMs) on complex, multi-hop question answering tasks by integrating structured knowledge graph retrieval with constrained reinforcement learning over the reasoning process.

Official BibTeX Citation:

@inproceedings{yu2025graphrag,
  title={GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learning},
  author={Yu, Chuanyue and Zhao, Kuo and Li, Yuhan and Chang, Heng and Feng, Mingjian and Jiang, Xiangzhe and Sun, Yufei and Li, Jia and Zhang, Yuzhi and Li, Jianxin and others},
  booktitle = {Proceedings of the ACM Web Conference 2026 (WWW '26)},
  year={2026}
}

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🤖 Model Weights & Usage

This repository provides LoRA adapters for the GraphRAG-R1 framework. To use them, you must load them onto the corresponding base model.

Available Adapters:

1. For Qwen2.5-7B: Adapter for the base model.
2. For Qwen2.5-7B-Instruct: Adapter for the instruction-tuned variant.

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🙏 Contact

For questions regarding the model or paper, please open an issue in the future GitHub repository or contact the authors.

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